Association between serum apolipoprotein B and atrial fibrillation: a case–control study

The relationship between apolipoprotein B (APOB) and atrial fibrillation (AF) is less well-known. We aimed to investigate the association between APOB and AF by gender. We conducted a case–control study including 1913 consecutive hospitalized patients to analyze the association between APOB and AF. 950 AF patients and 963 age-, sex-matched non-AF patients with sinus rhythm were evaluated. T-test, Mann–Whitney test, ANOVA, and Chi-square analysis were performed to analyze baseline data and intergroup comparisons. Pearson's correlation tests or Spearman correlation tests were performed to determine the interrelationships. Multiple regression analysis was performed to adjust for covariables. The receiver operator characteristic (ROC) curve was constructed to examine the performance of APOB. AF patients had lower APOB (P < 0.001) and an independent negative association between APOB and AF in both genders adjusting for confounding factors (OR 0.121, 95% CI 0.067–0.220, P < 0.001), regardless of statin use. APOB was positively correlated with total cholesterol (TC) (r = 0.529, p < 0.001), low-density lipoprotein cholesterol (LDL-C) (r = 0.545, p < 0.001), apolipoprotein A1 (APOA1) (r = 0.083, p < 0.001), and albumin (ALB) (r = 0.134, p < 0.001). ROC curve analysis showed that APOB level = 0.895 g/L was the most optimal cut-off value, the area under the ROC curve was 0.722. This study shows a protective association of APOB with AF in men and women. It implies APOB may be a potential biomarker for AF with a promising cut-off point of 0.895 g/L and may involve initiating and maintaining AF along with several metabolic factors.

Statistical analysis. All statistical analyses were conducted using SPSS software (version 26.0; SPSS Inc., Chicago, IL, USA). Specifically, continuous data were presented as mean ± standard deviations (SD) or medians and interquartile ranges (IQR) and compared by analysis of T-test or Mann-Whitney test and analysis of variance (ANOVA). Categorical data were expressed as percentages and compared by chi-square analysis. Meanwhile, Pearson correlation tests or Spearman correlation tests were performed to investigate interrelationships. Multivariate regression analyses were used to adjust for covariates. Additionally, the receiver operating characteristic (ROC) curve model was performed to explore the performance of the serum APOB. A two-tailed p-value < 0.05 was considered significant.
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Correlation between serum APOB and AF patients with non-receiving statins.
The ROC curve model for APOB levels predicting AF. Figure 2 showed the ROC curve model for APOB levels predicting AF. The ROC curve analysis showed that APOB level = 0.895 g/L was the most optimal cut-off value for predicting AF. The area under the ROC curve for the model was 0.722 (95%CI: 0.70-0.74, P < 0.05), and the sensitivity was 0.699, the specificity was 0.630. www.nature.com/scientificreports/ Correlation between serum APOB and AF related factors. Figure 3 showed the correlation between serum APOB and AF related factors. Our results suggested that APOB was positively correlated with TC (r = 0.529, p < 0.001, Fig. 3A), LDL-C (r = 0.545, p < 0.001, Fig. 3B), APOA1 (r = 0.083, p < 0.001, Fig. 3C), and ALB (r = 0.134, p < 0.001, Fig. 3D).
Spearman correlation analysis to evaluate the association of APOB with metabolic factors by gender in AF patients. As shown in Table 4 Spearman correlation analysis to evaluate the association of APOB with metabolic factors by gender in AF patients. As shown in Table 5, patients with AF of lower APOB had lower PAB, ALB, TG, TC, and LDL-C in both sexes (P < 0.001), as well as lower APOA1 and SUA in the men (P < 0.001).

Discussion
This study was the first to systematically investigate the effects of serum APOB on AF and the correlations between serum APOB and metabolic factors associated with AF by gender. The main findings of the present study were a protective association between serum APOB and AF in both sexes, regardless of statin use. Further results indicated that serum APOB was positively correlated with TC, LDL-C, APOA1, and ALB. In addition, it also showed that APOB was positively correlated with PAB, ALB, TG, TC, and LDL-C in male and female patients with AF. The area under the ROC curve of APOB was 0.722, the most optimal cut-off value was 0.895 g/L, the sensitivity was 0.699, and the specificity was 0.630. These findings suggested that the level of APOB and some related metabolic factors may decrease in the preclinical stage of AF. The effects of dyslipidemia on AF have been controversial. Although increased TC and LDL-C were recognized as the significant risk factors for AF 25,26 , several scholars also offered a contrary opinion 27,28 , which was called the "cholesterol paradox". In recent years, a growing body of data from studies has reported that low levels of LDL-C were strongly associated with an increased risk of AF 29,30 . Our results showed lower levels of TG, TC, LDL-C, and HDL-C in AF patients, which were well in alignment with earlier findings.
APOB, an important element in LDL and a precursor of atherosclerosis, reflects the number of lipoprotein particles that may induce atherosclerosis 31 .To our knowledge, few studies have reported the relationship between serum APOB and AF. Fortunately, we found several previous studies that reported relevant results. Two Mendelian randomization (MR) analyses indicated no significant causal effects of serum APOB on the risk of AF 32,33 . This result was inconsistent with our findings, there are several possible reasons. First, the main possible reason is racial differences; our patients were from China, while their study population was European populations, which contributes to heterogeneity in the study population. Second, the influence of several drugs was not evaluated in their MR study. Additionally, the study methods are significantly different, possibly related to the choice of covariates included in the models. Another nested cohort study suggested that low serum APOB was the main Table 4. Spearman correlation analysis to evaluate the association of APOB with metabolic factors by gender in AF patients. Data were presented as mean ± SD. Abbreviations as in Table 1. *Statistically significant value (P < 0.05).  Table 5. One-way ANOVA for subgroups to investigate the association between APOB levels and metabolic factors in AF patients. Data were presented as mean ± SD. Abbreviations as in Table 1. *Statistically significant value (P < 0.05).   34 . Our result was consistent with this study, which showed an independent negative association between serum APOB and AF in both sexes. In addition, we built the ROC model for APOB levels predicting AF. To our knowledge, there are no reports of APOB levels predicting AF. Our results indicated the area under the ROC curve was 0.722; when the most optimal cut-off value of the APOB level was 0.895 g/L, the sensitivity was 0.699, and the specificity was 0.630. Certainly, the current result requires further verification. Indeed, several underlying mechanisms may be considered to explain this interesting finding. Firstly, it has been well established that the effects of inflammation and oxidation stress on the complexity of AF [35][36][37] . Although most studies have suggested that there was almost no correlation between lipids and inflammatory markers [38][39][40] , several scholars still examined the association between APOB and inflammation. Faraj M et al. reported that APOB was a strong and independent predictor of several inflammatory markers such as interleukin-6 and CRP in postmenopausal overweight and obese women 41 . Further research indicated that reduced serum APOB was closely related to inflammation and increased serum APOB may be the key therapeutic target to reduce obesityrelated inflammation 42 . Therefore, it could be speculated that reduced APOB may initiate and maintain the inflammatory chain of AF. Secondly, the current results showed the lower levels of HDL-C in AF participants. Thus, we hypothesized that the loss of anti-inflammatory and antioxidant effects of HDL-C increased the formation of AF matrix [43][44][45] , and may contribute to the formation of AF risk factors such as heart failure 14,46 . Meanwhile, potential confounding factors such as statin use, lifestyle, and dietary factors may also confound the results. Consequently, it is essential to conduct further studies to explore the potential mechanisms.
We observed several AF-related confounders and adjusted for them in the regression analysis model. Current results indicated that AST levels were higher in AF patients. In fact, the relationship between liver enzymes and AF is unclear. Sinner et al. 47 reported transaminase concentrations are related to the increased risk of AF. The results of our study are supported by their study reporting higher AST levels in AF patients. Possible reasons mainly include preclinical heart failure 48,49 , metabolic syndrome, inflammation, oxidative stress, nonalcoholic fatty liver disease, strenuous exercise, overwork, drinking, greasy diet, irregular work and rest, and anger [50][51][52][53] . The current results also showed AF patients have more comorbidities including hypertension, coronary heart disease, and diabetes. Previous studies have demonstrated hypertension, coronary heart disease, and diabetes are associated with a higher risk of AF [54][55][56] . Moreover, these comorbidities are likely to form a vicious cycle with AF. Therefore, it would be interesting to investigate the association between APOB and lone AF in the future.
Additionally, we also paid attention to the correlation between serum APOB and AF-related factors. The findings indicated that serum APOB was positively correlated with TC, LDL-C, APOA1, and ALB. On this basis, we further investigated the potential relationship between APOB level and metabolic factors in men and women with AF by Spearman correlation analysis and One-way ANOVA for subgroups. We observed that APOB was positively correlated with PAB, ALB, TG, TC, and LDL-C in male and female patients with AF. These findings imply that serum APOB may be affected by PAB, ALB, TG, TC, and LDL-C, and participate in the pathological process of AF together.
Certainly, there might be some potential limitations worth considering. First, this was a single-center case-control study and this protective association outcome can't confirm causality. Second, we did not examine indicators of inflammation and oxidative stress. Third, medication characteristics and comorbidities of the AF patients and controls were not well matched; it would be interesting to investigate the association between APOB and AF based on patients with matched medication and comorbidities in the future. Fourth, several potential confounding factors such as genetic factors, lifestyle, medication, and family history may also have influenced the current results. Nevertheless, it did provide us with a new perspective to find the potential mechanisms of AF. Further prospective longitudinal cohort studies are encouraged to be conducted. In addition, the correlation between serum APOB and AF-related metabolic factors is still worthy of further studies, which will be useful to further clarify the relationship between serum APOB and AF.

Conclusions
In conclusion, we systematically investigated the association between serum APOB and AF. The present results indicated a protective association between serum APOB and AF in both sexes, regardless of statin use. Further findings showed that serum APOB was positively correlated with TC, LDL-C, APOA1, and ALB. These findings suggested serum APOB may be a potential biomarker for AF with a promising cut-off point of 0.895 g/L and may involve in the pathological progress of AF along with several metabolic factors. If a causal relationship between APOB and AF is confirmed, modulating APOB levels may contribute to the prevention or treatment of AF.

Data availability
The datasets are not publicly available due to them containing information that could compromise research participant privacy, but the minimal data are available from the corresponding author on reasonable request. www.nature.com/scientificreports/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.